


'''simple docstring'''


from __future__ import annotations







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= len(SCREAMING_SNAKE_CASE__	)
				# We need to create solution object to save path.
				_SCREAMING_SNAKE_CASE					:			Tuple							= [[0 for _ in range(SCREAMING_SNAKE_CASE__	)] for _ in range(SCREAMING_SNAKE_CASE__	)]
				_SCREAMING_SNAKE_CASE					:			List[str]							= run_maze(SCREAMING_SNAKE_CASE__				, 0				, 0				, SCREAMING_SNAKE_CASE__	)
				if solved:
								print("""\n""".join(str(SCREAMING_SNAKE_CASE__	) for row in solutions	)	)
				else:
								print("""No solution exists!"""	)
				return solved







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[Any]							= len(SCREAMING_SNAKE_CASE__	)
				# Final check point.
				if i == j == (size - 1):
								_SCREAMING_SNAKE_CASE					:			Tuple							= 1
								return True

				_SCREAMING_SNAKE_CASE					:			List[str]							= (not i < 0) and (not j < 0)  # Check lower bounds
				_SCREAMING_SNAKE_CASE					:			Any							= (i < size) and (j < size)  # Check upper bounds

				if lower_flag and upper_flag:
								# check for already visited and block points.
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= (not solutions[i][j]) and (not maze[i][j])
								if block_flag:
												# check visited
												_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 1

												# check for directions
												if (
												    run_maze(SCREAMING_SNAKE_CASE__				, i + 1				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
												    or run_maze(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, j + 1				, SCREAMING_SNAKE_CASE__	)
												    or run_maze(SCREAMING_SNAKE_CASE__				, i - 1				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
												    or run_maze(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, j - 1				, SCREAMING_SNAKE_CASE__	)
												):
																return True

												_SCREAMING_SNAKE_CASE					:			Optional[int]							= 0
												return False
				return False


if __name__ == "__main__":
				import doctest

				doctest.testmod()








'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_						:  str				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  Optional[int]				  =						{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: List[str]       =    """lilt"""






							def __init__(       self       ,   __snake_case=3_0522       ,   __snake_case=768       ,   __snake_case=12       ,   __snake_case=12       ,   __snake_case=3072       ,   __snake_case="gelu"       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=512       ,   __snake_case=2       ,   __snake_case=0.02       ,   __snake_case=1e-12       ,   __snake_case=0       ,   __snake_case="absolute"       ,   __snake_case=None       ,   __snake_case=4       ,   __snake_case=1024       ,   **__snake_case       ,   ):
											super().__init__(pad_token_id=__snake_case       ,   **__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= vocab_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= num_hidden_layers
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= num_attention_heads
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_act
											_SCREAMING_SNAKE_CASE					:			Dict							= intermediate_size
											_SCREAMING_SNAKE_CASE					:			Tuple							= hidden_dropout_prob
											_SCREAMING_SNAKE_CASE					:			List[str]							= attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= max_position_embeddings
											_SCREAMING_SNAKE_CASE					:			Dict							= type_vocab_size
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= initializer_range
											_SCREAMING_SNAKE_CASE					:			Tuple							= layer_norm_eps
											_SCREAMING_SNAKE_CASE					:			str							= position_embedding_type
											_SCREAMING_SNAKE_CASE					:			List[str]							= classifier_dropout
											_SCREAMING_SNAKE_CASE					:			int							= channel_shrink_ratio
											_SCREAMING_SNAKE_CASE					:			str							= max_ad_position_embeddings




